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Article

Relationship of Oedaleus decorus asiaticus Densities with Soil Moisture and Land Surface Temperature in Inner Mongolia, China

by
Bobo Du
1,†,
Yanmin Shan
2,†,
Wenjiang Huang
3,
Yingying Dong
3,
Shujing Gao
1,
Yuchao Yue
2,
Jing Guo
3,
Liwa Ga
2,
Yan Zhang
3 and
Haibin Han
1,*
1
Institute of Grassland Research, Chinese Academy of Agricultural Sciences, Hohhot 010010, China
2
Inner Mongolia Forestry and Grassland Pest Control and Quarantine Station, Hohhot 010010, China
3
Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2025, 15(4), 998; https://doi.org/10.3390/agronomy15040998
Submission received: 26 March 2025 / Revised: 17 April 2025 / Accepted: 18 April 2025 / Published: 21 April 2025
(This article belongs to the Section Water Use and Irrigation)
Browse Figures
Review Reports Versions Notes

Abstract

:
Oedaleus decorus asiaticus (O. decorus) is a significant pest in the grasslands of Inner Mongolia, posing considerable challenges to the development of animal husbandry. To understand the key factors influencing the population distribution of O. decorus, field surveys were conducted from 2018 to 2020, during which the population count, growth stage, and location information of O. decorus were recorded. Daily soil moisture (SM) data and daily land surface temperature (LST) data were obtained from the National Tibetan Plateau Data Center, and a Generalized Additive Model (GAM) was constructed. Our findings indicate that the SM (S8) in August of the previous year is the most critical factor, with an F-value of 27.422, followed by the LST (L10) in October of the previous year, the LST (L6) in June of the survey year, the SM (S9) in September of the previous year, the LST (L3) in March of the survey year, and the LST (L5) in May of the survey year, with F-values of 7.848, 7.223, 5.823, 4.919, and 3.547, respectively. S8 and S9 can be regarded as vital indicators for predicting and monitoring the occurrence of O. decorus. However, the contributions of S8 and S9 to O. decorus density differ considerably. S8 is negatively correlated with O. decorus density, while S9 values below 0.29 m3/m3 can promote the growth of O. decorus. A higher LST during early overwintering correlates with increased O. decorus density. During the survey year, LST emerged as the primary factor affecting grasshopper density. Additionally, it plays a more complex role during incubation periods. This study clearly identifies SM and LST as the major factors influencing the occurrence of O. decorus, which will aid in predicting and monitoring its density.

1. Introduction

1.1. Threats Posed by Grasshoppers to Ecosystems

Grasshoppers are among the most significant pests worldwide, posing substantial threats to the agriculture and livestock industries, particularly in arid and semi-arid regions [1]. Although many studies have suggested that sparse populations and pre-third-instar nymphs of grasshoppers can promote pasture growth and development [2,3], their clustering and migration stages can lead to serious problems for various ecological systems, including agroecosystems and grassland ecosystems [4,5]. Therefore, controlling grasshopper density and preventing their migration are helpful for reducing the risk of grasshopper outbreaks [6]. Previous research has shown that accurately identifying grasshopper breeding sites and population densities is critical for preventing their aggregation and migration [7,8].

1.2. Climate Variables Affecting Grasshopper Occurrence

In order to accurately identify potential areas for grasshopper outbreaks, numerous studies have been conducted both in China and internationally [9,10]. These studies have concluded that grasshopper outbreaks are complex phenomena significantly influenced by environmental factors [1,11,12,13]. In particular, climate variables such as precipitation and temperature have been shown to directly affect grasshopper populations [14]. Research indicates that outbreaks of the Oriental migratory locust may occur when precipitation prior to the incubation period is lower than that of a typical year [15]. Additionally, some studies have demonstrated that grasshopper eggs struggle to hatch when regional average rainfall is significantly higher than in normal years, which can even lead to egg mold [16]. In other words, grasshopper populations exhibit a significant negative correlation with rainfall throughout their life cycle, particularly during the nymph and egg stages [14]. Temperature is another critical climatic factor influencing grasshopper spawning, overwintering, incubation, and the vitality of both nymphs and adults [2,17,18,19]. Grasshoppers tend to prefer warmer areas for spawning, while extremely low temperatures can inhibit egg-laying [2,20]. If the temperature during the overwintering period is higher than in a typical year, the grasshopper population is likely to increase [21,22]. Furthermore, during the incubation and growth phases surface temperature plays a major role in determining grasshopper populations [17]. Therefore, these studies suggest a strong correlation between precipitation and temperature conditions and grasshopper occurrences. However, a previous study found a weaker relationship between grasshoppers and temperature, which may be attributed to the influence of other moderating environmental factors [23].
Some studies have utilized the drought index to investigate the relationship between grasshopper populations and rainfall, concluding that the drought index possesses predictive power [24,25]. In various regions around the world, grasshopper outbreaks frequently follow periods of droughts [26,27,28]. Drought has been recognized as a significant indicator for predicting and monitoring grasshopper occurrences. Among the critical drought indicators, soil moisture (SM) has been extensively employed in identifying pest habitat areas [7,22]. Consequently, researchers have examined the relationship between SM and grasshopper occurrences [15,29]. These studies have demonstrated that SM is a vital environmental factor throughout the grasshopper life cycle, influencing the selection of spawning areas, the survival rate of eggs during winter, the hatching rate of eggs, and the habitat selection of habitats for adults [30]. Optimal SM conditions promote egg-laying, overwintering, incubation, and the development of grasshopper nymphs and adults [29]. Conversely, excessive SM can lead to the onset of diseases in grasshoppers during their life cycle [31]. Therefore, substantial evidence supports the strong correlation between SM content and grasshopper occurrences.

1.3. Main Aim of This Study

Oedaleus decorus asiaticus (commonly referred to as O. decorus) is one of the most significant harmful insects in the arid and semi-arid regions of northern China. On average, an individual O. decorus consumes more than 8.8 g of dried forage throughout its life cycle, with a minimum consumption of 6.3 g and a maximum consumption of 12.4 g [32]. Additionally, O. decorus is capable of migrating over long distances [33]. Consequently, outbreaks of O. decorus pose severe threats to animal husbandry and agriculture. A fundamental requirement for monitoring these outbreaks is the identification of key environmental factors that influence their distribution and the extent of their impact. This study explores the response of grasshopper populations to soil moisture (SM) and land surface temperature (LST). To improve our understanding of the effects of SM and LST on grasshopper densities across different temporal scales, both monthly and daily data for SM and LST were utilized. The findings are expected to improve our knowledge of how these factors influence grasshopper occurrence and population dynamics.

2. Material and Methods

2.1. Field Experiment

Inner Mongolia is situated in northern China, bordering Russia and Mongolia to the north. It features a warm temperate continental monsoon climate, characterized primarily by cold winters with minimal precipitation and hot, dry summers. Grasshoppers are major pests in the region, with O. decorus being a notable species due to its capacity for long-distance migration and high feeding rates, which lead to forage reduction. Field experiments were conducted during the second week of June from 2018 to 2020 in the central and eastern regions of Inner Mongolia (Figure 1). We surveyed the population density and growth stages of O. decorus per square meter by visual observation and box quadrat methods. Ultimately, 107 suitable sites were selected for this study, with the numbers of survey sites from 2018 to 2020 being 63, 24, and 20, respectively. During the investigation period, the predominant growth stage of O. decorus was the second instar nymphal stage, with a few third-instar nymphs present. Since nymphs lack strong migratory abilities before reaching the third instar stage, the locations where they were found were considered breeding areas.
In order to further understand the impact of soil moisture (SM) and land surface temperature (LST) on various grasshopper densities and the life cycle of grasshoppers, previous studies were referenced to categorize the field survey sites into eight levels based on O. decorus density [3,34]. The classification of grasshopper occurrence density, along with the number of field survey points for each density level, is presented in Table 1.

2.2. SM and LST Datasets

All daily surface soil moisture (SM) and land surface temperature (LST) datasets covering the period from 2017 to 2020 were downloaded, which are production remote sensing datasets from the National Tibetan Plateau Data Center (SM: https://data.tpdc.ac.cn/en/data/e1f24e35-6235-40b2-b3d7-677dfb249e39 (accessed on 17 April 2025), LST: https://data.tpdc.ac.cn/zh-hans/data/05d6e569-6d4b-43c0-96aa-5584484259f0 (accessed on 17 April 2025)) [35,36]. The SM dataset was generated from MODIS products through the downscaling of passive microwave data from AMSR-E and AMSR-2. The LST dataset was derived from Terra/Aqua MODIS LST products, the GLDAS dataset, and other satellite remote sensing data, including vegetation indices and reflectance data. Both the SM and LST datasets were stored in HDF format and cover the region encompassing China’s landmass and its surrounding areas, with a spatial resolution of 1 km. Python (version 3.8) was utilized to convert the HDF data to TIFF format. All SM data were multiplied by 0.001 to obtain the soil moisture content per unit volume (cm3/cm3). The LST data were divided by 100 and then adjusted by subtracting 273.15 to convert the values to degrees Celsius. Using the ‘gdal’ package (version 3.6.0) in Python, daily and monthly SM and LST data were extracted from the SM and LST datasets for each site of O. decorus occurrence. The average SM and LST for each month were calculated. Twelve monthly variables of SM and LST during the spawning, overwintering, incubation, and growth stages of O. decorus were selected (Table 2), taking into account the variable responses of grasshoppers to SM and LST throughout their life cycle.

2.3. Statistical Analyses

2.3.1. Environmental Factors and Screening

The Pearson correlation method was used to identify variables with correlation coefficients less than 0.8 [37]. Consequently, environmental factors with weaker correlations were selected to construct a Generalized Additive Model (GAM) to mitigate the impact of strong collinearity on the model’s accuracy [38].

2.3.2. Generalized Additive Model

To assess the effects of SM and LST on grasshopper density, a GAM with a quasi-Poisson distribution family using the ‘mgcv’ package (version 1.8.42) in R environment (version 4.2.1) [39] was constructed. The GAM is a semi-parametric extension of the Generalized Linear Model (GLM) and is composed of smooth functions [38]. This model can accommodate environmental factors with non-linear relationships and reveal the significance of different variables. The estimated degrees of freedom (edf) and reference degrees of freedom (rdf) were calculated, which can represent the linear or non-linear relationships between grasshopper density and each parameter. Additionally, the F-value was determined to estimate the contribution of different factors. The higher the F-value, the greater the contribution. The GAM can be expressed as follows:
g ( u ) = a 0 + f 1 ( x 1 ) + f 2 ( x 2 ) + … + f n ( x n ) + ε
where g(…) represents the link function, u is the expected value of the response variable (grasshopper density), a0 means the intercept, f(…) is the smooth function of explanatory variable xi, and xi denotes the explanatory variable.

2.3.3. Optimal GAM Construction

In this study, the spline function was applied as the smooth function. Regarding the smooth parameter (k) for smooth functions, grasshopper density was regressed with various SM and LST factors by the GAM, and the curve with biological significance was selected. Subsequently, these smooth parameters (k) were used to establish a multi-variable GAM. The accuracy of the GAM was evaluated using Generalized Cross Validation (GCV). The lower the value of GCV, the higher the accuracy of the GAM.
We constructed multiple GAMs including a single environmental factor and multiple environmental factors as input parameters. According to the smallest GCV and the principle of the optimal model, all factors were input variables without strong collinearity and significantly contributed to model construction.

3. Results

3.1. Screening of Environmental Factors

As illustrated in Figure 2, seventeen pairs of factors exhibiting strong collinearity were identified: for example, S1 is correlated with S2, S11, and S12; S2 is correlated with S3, S11, and S12; S3 is correlated with S11 and S12; S9 is correlated with L5, L7, L9, and S10; S11 is correlated with S12; L1 is correlated with L12; L2 is correlated with L12; and L9 is correlated with L5 and L10. Due to the presence of strong collinearity, these factors could not be included simultaneously in the model.

3.2. GAM Validation

The minimum GCV value of the optimal GAM is 6.67. The results appear to suggest that these environmental factors significantly influence (p < 0.05) O. decorus density (Table 3). These factors explained 82.4% of the deviance (R2 = 0.821). In other words, these factors can explain 82.4% of the variance in O. decorus density at the logarithm level. The model has a good fitting.

3.3. Relationship Between O. decorus Density and Monthly Average SM and LST

As shown in Table 3, S8 and L3 have a linear relationship with the logarithm of O. decorus density (edf = 1.000). In contrast, the other factors exhibit a non-linear relationship with the logarithm of O. decorus density (edf > 1.000). In addition, the six environmental factors influenced O. decorus density to different extents. Estimates of the F-value of all environmental factors indicate that S8 has the strongest prediction ability for O. decorus density, followed by L10, L6, S9, L3, and L5.
Scatter plots of the GAM results were generated to further understand the association between the six environmental factors and O. decorus density. From the perspective of the relationship between the logarithm of O. decorus density and S8 (Figure 3a), the logarithm of O. decorus density exhibited a downward trend with the increase in SM content. In August of the previous year with reference to the survey year, the SM of locations with O. decorus occurrence ranged from 0.21 to 0.27 m3/m3. Interestingly, the opposite phenomenon was observed in Figure 3b, whereas in September of the previous year the logarithm of O. decorus density showed a fluctuating upward trend with increasing SM. A clear inflection was observed at points with a SM content of 0.29 m3/m3, after which the logarithm of O. decorus density rapidly declined. As shown in Figure 3c, the logarithm of O. decorus density exhibited a positive relationship with L10. The logarithm of O. decorus density showed an upward trend with the gradual rise in temperature. Nevertheless, when the average temperature in October was higher than 19 °C, the growth trend of the logarithm of O. decorus density slowed down. As shown in Figure 3d, the logarithm of O. decorus density showed a negative correlation with L3, reflecting a decrease in O. decorus population with increasing temperature. Regarding the relationship between the logarithm of O. decorus density and L5 (Figure 3e), the average LST in May (below 33 °C) could promote a logarithmic increase in O. decorus density. In contrast, an average LST higher than 33 °C could lead to a slow decrease. The logarithm of O. decorus density showed a more complex relationship with L6 than L5 (Figure 3f). The logarithm of O. decorus density showed significant growth at a LST below 36.8 °C, but it showed a rapid decline above 36.8 °C. However, this phenomenon continued only up to 39.8 °C, beyond which the logarithm of O. decorus population exhibited smaller changes.

3.4. Roles of SM and LST in O. decorus Densities

This study also evaluated the relationships between various O. decorus densities and daily environmental factors, including soil moisture (SM) during August and September of the year prior to the survey, and land surface temperature (LST) in October of the preceding year, as well as in May and June of the survey year.

3.4.1. Relationship Between O. decorus Densities and Daily SM

As illustrated in Figure 4, the average daily SM for each density level has been calculated. For the O. decorus occurrence density in the range of [0, 8), the daily soil moisture (SM) content was primarily concentrated around 0.245 m3/m3 in August (Figure 4a). With the increase in population density from [8, 15) to [15, 22), the daily SM content gradually decreased, with median values of approximately 0.235 m3/m3 and 0.22 m3/m3, respectively. In contrast, for the O. decorus density of [22, 30) the daily SM content increased slightly, fluctuating between 0.217 and 0.228 m3/m3, with a median value of about 0.225 m3/m3. The O. decorus density was [30, 45) when the SM content was at its maximum in August, ranging from 0.245 to 0.260 m3/m3 with a median of approximately 0.255 m3/m3. From then on, as population density continued to rise, the daily SM content began to decline. Furthermore, there was a considerable variation in SM content between lower grasshopper density (less than 30 individuals/m2) and higher grasshopper density (greater than 30 individuals/m2). According to Figure 4b, the daily SM in September for densities of [0, 8) and [8, 15) ranged from 0.230 to 0.280 m3/m3 and 0.215 to 0.235 m3/m3, respectively, with median values of approximately 0.250 m3/m3 and 0.235 m3/m3. As population density increased from [15, 22) to [45, 60), the daily SM content also increased. At grasshopper densities below 60 nymphs/m2, the O. decorus population was larger in areas with higher SM content. Conversely, SM content was lower at certain surveyed sites where populations ranged from 60 to 90 individuals/m2. At our survey points, SM content was higher when grasshopper density was in the range of [90, 150). Therefore, it can be inferred from Figure 4b that locations with a large grasshopper population (O. decorus density greater than 30 nymphs/m2) exhibit higher SM content in September.

3.4.2. Relationship Between O. decorus Densities and Daily LST

As illustrated in Figure 5, the mean daily LST for each density level has been computed. O. decorus are ectothermic creatures that prefer to oviposit in areas with higher ambient temperatures. Therefore, the daily land surface temperatures (LSTs) (Figure 5a–d) were analyzed to understand the impact of LST on habitat selection by O. decorus. The median values of daily L10 varied considerably across different density levels (Figure 5a). L10 increased notably at higher-density levels, specifically from the range of [0, 8) to [22, 30). An inflection point was observed at grasshopper densities of ≥30 nymphs/m2 and <45 nymphs/m2. When grasshopper density exceeded 30 individuals/m2, L10 decreased as population density increased. The relationship between grasshopper density levels and daily L3 is illustrated in Figure 5b. The median values of LST at various density levels increased when O. decorus density was less than 22 nymphs/m2. However, the median values of L3 decreased when grasshopper density surpassed 22 nymphs/m2. Compared to other environmental factors, the daily values of LST at different O. decorus density levels were most concentrated in May (Figure 5c). Under low-density conditions, particularly when grasshopper density was below 30 nymphs/m2, higher LSTs appeared to predict a greater nymph density. Nevertheless, at the study sites the daily L5 declined gradually as grasshopper density increased from 30 nymphs/m2 to 150 nymphs/m2. The daily LST at varying O. decorus density levels exhibited more fluctuations in June than in other months (Figure 5d). The median values of daily L6 varied with grasshopper density levels, showing the largest fluctuations within the density ranges of [0, 8) to [22, 30), with temperatures ranging from 37.4 °C to 40.6 °C. In contrast, smaller fluctuations were observed at higher-density levels of [30, 45) to [90, 150), with median values of 37 °C and 38.4 °C, respectively.

4. Discussion

4.1. Role of Environmental Factors in the GAM

Grasshoppers are ectothermic insects that directly rely on suitable conditions for oviposition, overwintering, incubation, and development [30,40]. Climate factors, especially precipitation and temperature, are the major determinants of grasshopper occurrence [9,41]. Changes in precipitation and temperature significantly influence SM content, which in turn affects grasshopper abundance [22,42]. In this study, SM and LST datasets were applied as environmental factors to analyze and discuss their effects on different O. decorus density levels. All factors showed a significant association (p < 0.05) with O. decorus populations in the model (Table 3). The factor with the largest contribution was L8 with an F-value of 27.422. This confirms that SM content in the previous summer directly affects the spatial distribution of O. decorus population density [15].

4.2. Influence of Environmental Factors on O. decorus Density

The grasshopper plague outbreak is a complex process influenced by various environmental factors, including vegetation, soil, precipitation, temperature, and terrain conditions [43]. SM and LST are particularly key factors that directly or indirectly affect the selection of oviposition sites, the survival of eggs, and the reproduction rates in subsequent generations [7,44]. However, there is a dearth of research focusing on the relationship between SM/LST and O. decorus density. In this study, we have identified the key factors of SM/LST that determine the density of O. decorus. Interestingly, there are considerable seasonal variations in the effects of LST and SM on the density of O. decorus. In August and September, SM is a primary factor influencing the selection of oviposition sites. Meanwhile, LST primarily affects the overwintering and incubation of eggs.
We analyzed the relationship between the density of O. decorus and six primary environmental factors on a monthly basis using the GAM in Section 3.3. S8 is a key factor determining the selection of spawning sites and the number of eggs laid by O. decorus. The higher the SM content in August of the previous year, the lower the density of O. decorus (Figure 3a). The negative correlation between S8 and O. decorus density may be explained by water content in the soil beyond the species’ optimal range, which reduces the spawning or prevents oviposition [17]. Because grasshoppers require a suitable soil environment for laying their eggs [45], another vital factor of SM influencing O. decorus density is S9. The density of O. decorus shows a fluctuating increase with the increase in S9. However, we can find that O. decorus density rapidly declined when S9 was greater than 0.29 m3/m3. As with other grasshoppers around the world, O. decorus density begins to rapidly decline when SM exceeds a certain threshold [46]. This indicates that abundant SM has adverse impacts on O. decorus occurrence. SM in early autumn can impact egg preservation, thereby avoiding water loss from eggs and increasing the survival of eggs during overwintering [44]. Higher water content in the soil during the egg stage could favor natural enemies, such as parasites, fungi, and bacteria, of O. decorus. This could indirectly affect the eggs, resulting in decreased survival or reproduction rates [15,29].
Another prior-year factor significantly affecting O. decorus density is LST (L10) in October. Under low-temperature conditions (L10 < 19 °C), the increase in temperature promotes the increase in O. decorus population. However, this growth trend of grasshopper density gradually slows down when L10 exceeds 19 °C. This result indicates that higher temperatures in early winter, specifically in October, can enhance the overwintering rate of grasshopper eggs. Previous studies have also found that the mean temperatures of the coldest quarter influence the distribution of grasshoppers [22,47]. With the increasing temperatures across the globe, warmer winters are becoming more common. This trend promotes a higher survival rate of eggs and enhances the reproductive capabilities of grasshoppers in the next generation, leading to an increased risk of grasshopper plague outbreaks [48].
In spring, LST is the main environmental factor affecting the density of O. decorus. LST exhibits a negative correlation with O. decorus density in March. It can be inferred from this result that higher temperatures in March may pre-incubate the eggs. However, late spring often experiences cold spells in Inner Mongolia. This phenomenon may lead to the termination of egg development or the death of nymphs of O. decorus. Interestingly, as the LST rises in May, O. decorus density first increases and then decreases or levels off. L6 is another crucial variable directly influencing the incubation of O. decorus, exhibiting a considerably non-linear relationship with O. decorus density. These results suggest that O. decorus density is greatly correlated with the changes in LST. Additionally, many studies have confirmed that higher temperatures and drought in early summer can increase the population density of grasshopper [49,50]. These results verify that the eggs of grasshopper require specific soil conditions, and higher temperatures could lead to soil desiccation, thus reducing the survivability of eggs.
To gain a deeper understanding of the relationship between the population density levels of O. decorus and the six environmental factors, the average daily SM and LST at different density levels of O. decorus were calculated in Section 3.4. Nevertheless, when comparing the response of grasshoppers at low-density levels (<30 nymphs/m2) and high-density levels (≥30 nymphs/m2) to SM content in Figure 4a, the values of L8 and L9 are lower at low-density levels than at high-density levels. Up to 30 nymphs/m2, an increase in SM content would reduce the grasshopper population. Beyond this, an increase in SM would promote the density growth of O. decorus. Therefore, the O. decorus population density of 30 nymphs/m2 is believed to be a very important inflection point. Accordingly, sites with relatively high SM levels are suggested to have abundant food, which would attract a large number of adult O. decorus to lay eggs [51]. Further investigation into this inflection point is warranted to clarify the influence of SM content on grasshopper populations.

4.3. Recommendations for O. decorus Prediction and Monitoring

The results confirm that SM and LST are significantly correlated with O. decorus population density, especially SM in August, which can be used to predict and monitor the dynamic distribution of O. decorus density. Nevertheless, grasshopper infestation is a complex process affected by other factors such as vegetation conditions, climatic factors, geographic and geomorphic conditions, and grasshopper abundance [14,24,50]. Therefore, in order to accurately monitor and predict dynamic changes in O. decorus density, comprehensive models should be established by integrating more source datasets, including vegetation, soil, meteorological, and geographic conditions. Furthermore, utilizing remote sensing data, big data, and artificial intelligence to predict outbreak areas and enhance real-time monitoring capabilities will become a primary method [52].
Unfortunately, our study, which is based on 3 years of monitoring (2018–2020), observed considerable correlations between the densities of O. decorus and SM/LST. While our findings are valid within the study period, the 3-year timeframe does not adequately capture fluctuations, such as droughts or heatwaves. Therefore, longer-term studies are necessary to assess the robustness of these patterns under climate variability in future research.

5. Conclusions

This study developed a GAM using six environmental factors (S8, S9, L10, L3, L5, and L6) as input parameters. These six factors accounted for 82.4% of the variance in O. decorus density at the logarithmic scale, indicating the model’s potential to predict O. decorus population density. Interestingly, there were considerable seasonal variations in the effects of LST and SM on the density of O. decorus. In August and September, SM showed a strong correlation with O. decorus density. It has emerged as a key factor influencing the selection of oviposition sites and serves as a vital indicator for predicting and monitoring O. decorus occurrences. Meanwhile, LST primarily impacted the overwintering and incubation of O. decorus eggs. The most considerable LST factor with strong predictive capability for O. decorus population density was L10, followed by L6, L3, and L5. Our study has identified the key factors of SM/LST that determine the density of O. decorus. These findings could provide valuable insights for monitoring and predicting future outbreaks of O. decorus. While our research was based on only three years of fieldwork (2018–2020), future studies could address data limitations by enriching the field survey data of O. decorus. Additionally, we could establish comprehensive models that integrate various environmental factors, such as vegetation, soil, climate, and topographical conditions, to monitor and predict the outbreak of O. decorus.

Author Contributions

Conceptualization, B.D., W.H. and Y.S.; methodology, Y.Y. and Y.S.; software, Y.D., S.G. and L.G.; writing—original draft, B.D.; data curation, H.H. and Y.Z.; supervision, W.H.; visualization, J.G. and Y.Z.; investigation, Y.S., Y.Y. and L.G.; project administration, H.H.; funding acquisition, H.H. and B.D.; writing—review and editing, W.H., J.G., Y.Z. and Y.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (42471369); the Inner Mongolia Nature Science Foundation, grant number 2024QN04017; the Central Public-interest Scientific Institution Basal Research Fund, grant number 1610332023010; and the Inner Mongolia Nature Science Foundation, grant number 2023MS03020.

Data Availability Statement

The authors do not have permission to share data.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Distribution of O. decorus survey sites from 2018 to 2020.
Figure 1. Distribution of O. decorus survey sites from 2018 to 2020.
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Figure 2. Pearson correlation analysis between any two parameters. (Note: Histograms along the diagonal indicate the distribution of individual environmental factors. Scatter plots below the diagonal show the relationships between pairs of factors, with the x-axis (left-to-right) representing the variable of the factor on the left and the y-axis (top-to-bottom) representing the variable of the factor below. The numbers above the diagonal indicate the Pearson correlation coefficient and significance levels (* p < 0.05, ** p < 0.01, *** p < 0.001)).
Figure 2. Pearson correlation analysis between any two parameters. (Note: Histograms along the diagonal indicate the distribution of individual environmental factors. Scatter plots below the diagonal show the relationships between pairs of factors, with the x-axis (left-to-right) representing the variable of the factor on the left and the y-axis (top-to-bottom) representing the variable of the factor below. The numbers above the diagonal indicate the Pearson correlation coefficient and significance levels (* p < 0.05, ** p < 0.01, *** p < 0.001)).
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Figure 3. Effect analysis of environmental factors on O. decorus density. (a) effect analysis of S8 on O. decorus density; (b) effect analysis of S9 on O. decorus density; (c) effect analysis of L10 on O. decorus density; (d) effect analysis of L3 on O. decorus density; (e) effect analysis of L5 on O. decorus density; (f) effect analysis of L6 on O. decorus density. (Note: The x-axis and y-axis indicate variations in environmental factors and the fitting value between environmental factors and the logarithm of O. decorus density, respectively. The curves represent smooth fitting between the independent variable and the logarithm of O. decorus density. The pink buffer on both sides of the curve indicates the confidence interval of the fitting value. The closer the estimated degree of freedom to 1, the closer the curve to the line; the larger the number of degrees of freedom, the stronger the curve).
Figure 3. Effect analysis of environmental factors on O. decorus density. (a) effect analysis of S8 on O. decorus density; (b) effect analysis of S9 on O. decorus density; (c) effect analysis of L10 on O. decorus density; (d) effect analysis of L3 on O. decorus density; (e) effect analysis of L5 on O. decorus density; (f) effect analysis of L6 on O. decorus density. (Note: The x-axis and y-axis indicate variations in environmental factors and the fitting value between environmental factors and the logarithm of O. decorus density, respectively. The curves represent smooth fitting between the independent variable and the logarithm of O. decorus density. The pink buffer on both sides of the curve indicates the confidence interval of the fitting value. The closer the estimated degree of freedom to 1, the closer the curve to the line; the larger the number of degrees of freedom, the stronger the curve).
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Figure 4. Distribution of daily SM values at diverse O. decorus density levels. (a) daily S8 values under different O. decorus density levels; (b) daily S9 values under different O. decorus density levels. (Note: Raincloud distributions with the ‘cloud’ representing data distribution, the ‘rain’ representing raw data of daily SM at different density levels, and the boxplot indicating the probability distribution of the raw data).
Figure 4. Distribution of daily SM values at diverse O. decorus density levels. (a) daily S8 values under different O. decorus density levels; (b) daily S9 values under different O. decorus density levels. (Note: Raincloud distributions with the ‘cloud’ representing data distribution, the ‘rain’ representing raw data of daily SM at different density levels, and the boxplot indicating the probability distribution of the raw data).
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Figure 5. Distribution of daily LST values at diverse O. decorus density levels. (a) daily L10 values under different O. decorus density levels; (b) daily L3 values under different O. decorus density levels; (c) daily L5 values under different O. decorus density levels; (d) daily L6 values under different O. decorus density levels. (Note: Raincloud distributions with the ‘cloud’ representing data distribution, the ‘rain’ representing raw data of LST at different density levels, and the boxplot indicating the probability distribution of the raw data).
Figure 5. Distribution of daily LST values at diverse O. decorus density levels. (a) daily L10 values under different O. decorus density levels; (b) daily L3 values under different O. decorus density levels; (c) daily L5 values under different O. decorus density levels; (d) daily L6 values under different O. decorus density levels. (Note: Raincloud distributions with the ‘cloud’ representing data distribution, the ‘rain’ representing raw data of LST at different density levels, and the boxplot indicating the probability distribution of the raw data).
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Table 1. O. decorus nymphs/m2 and their density levels.
Table 1. O. decorus nymphs/m2 and their density levels.
Species Density/m2Number of Survey SitesCumulative Survey Sites
201820192020
[0, 8)99422
[8, 15)231226
[15, 22)011920
[22, 30)102012
[30, 45)71210
[45, 60)1359
[60, 90)0213
[90, 150)0505
Table 2. Monthly soil moisture and land surface temperature.
Table 2. Monthly soil moisture and land surface temperature.
MonthSoil MoistureLand Surface Temperature
July of the year preceding the yearS7L7
August of the year preceding the yearS8L8
September of the year preceding the yearS9L9
October of the year preceding the yearS10L10
November of the year preceding the yearS11L11
December of the year preceding the yearS12L12
January of the yearS1L1
February of the yearS2L2
March of the yearS3L3
April of the yearS4L4
May of the yearS5L5
June of the yearS6L6
Note: S and L represent soil moisture and land surface temperature, respectively. The numbers in the lower-right corner of S and L indicate the month.
Table 3. Analysis of the GAM between O. decorus density and environmental factors.
Table 3. Analysis of the GAM between O. decorus density and environmental factors.
Smoothing Effect TermEstimated Degree of Freedom (edf)Reference Degrees of Freedom (rdf)FP
S81.0001.00027.4221.43 × 10−6 ***
S97.9939.5985.8232.69 × 10−6 ***
L101.9292.4337.8480.000445 ***
L31.0001.0004.9190.029360 *
L53.2264.0523.5470.012227 *
L64.0004.0007.2234.97 × 10−5 ***
Note: * p < 0.05 and *** p < 0.001.
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Du, B.; Shan, Y.; Huang, W.; Dong, Y.; Gao, S.; Yue, Y.; Guo, J.; Ga, L.; Zhang, Y.; Han, H. Relationship of Oedaleus decorus asiaticus Densities with Soil Moisture and Land Surface Temperature in Inner Mongolia, China. Agronomy 2025, 15, 998. https://doi.org/10.3390/agronomy15040998

AMA Style

Du B, Shan Y, Huang W, Dong Y, Gao S, Yue Y, Guo J, Ga L, Zhang Y, Han H. Relationship of Oedaleus decorus asiaticus Densities with Soil Moisture and Land Surface Temperature in Inner Mongolia, China. Agronomy. 2025; 15(4):998. https://doi.org/10.3390/agronomy15040998

Chicago/Turabian Style

Du, Bobo, Yanmin Shan, Wenjiang Huang, Yingying Dong, Shujing Gao, Yuchao Yue, Jing Guo, Liwa Ga, Yan Zhang, and Haibin Han. 2025. "Relationship of Oedaleus decorus asiaticus Densities with Soil Moisture and Land Surface Temperature in Inner Mongolia, China" Agronomy 15, no. 4: 998. https://doi.org/10.3390/agronomy15040998

APA Style

Du, B., Shan, Y., Huang, W., Dong, Y., Gao, S., Yue, Y., Guo, J., Ga, L., Zhang, Y., & Han, H. (2025). Relationship of Oedaleus decorus asiaticus Densities with Soil Moisture and Land Surface Temperature in Inner Mongolia, China. Agronomy, 15(4), 998. https://doi.org/10.3390/agronomy15040998

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